EigenExpressions for Facial Expression Recognition

We propose an algorithm for facial expression recognition which can classify the given image into one of the seven
basic facial expression categories (happiness, sadness, fear, surprise, anger, disgust and neutral). PCA is used
for dimensionality reduction in input data while retaining those characteristics of the data set that contribute
most to its variance, by keeping lower-order principal components and ignoring higher-order ones. Such low-order
components contain the "most important" aspects of the data. The extracted feature vectors in the reduced space
are used to train the supervised Neural Network classifier.

This approach results extremely powerful because it
does not require the detection of any reference point or node grid. The proposed method is fast and can be used
for real-time applications. This code has been tested using the JAFFE Database, available at
http://www.kasrl.org/jaffe.html. Using 150 images randomly selected for training and 63 images for testing,
without any overlapping, we obtain an excellent recognition rate greater than 83%. The semantic data
ratings for this database are available at http://www.kasrl.org/jaffe_info.txt.